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Understanding team behaviors and dynamics are important to better understand and foster better teamwork. The goal of this master's thesis was to contribute to understanding and assessing teamwork in small group research, by analyzing motion dynamics and team performance with non-contact sensing and computational assessment. This thesis's goal is to conduct an exploratory analysis of motion dynamics on teamwork data to understand current limitations in data gathering approaches and provide a methodology to automatically categorize, label, and code team metrics from multi-modal data. We created a coding schema that analyzed different teamwork datasets. We then produced a taxonomy of the metrics from the literature that classify teamwork behaviors and performance. These metrics were grouped on whether they measured communication dynamics or movement dynamics. The review showed movement dynamics in small group research is a potential area to apply more robust computational sensing and detection approaches. To enhance and demonstrate the importance of motion dynamics, we analyzed video and transcript data on a publicly available multi-modal dataset. We determined areas for future study where movement dynamics are potentially correlated to team behaviors and performance. We processed the video data into movement dynamic time series data using an optical flow approach to track and measure motion from the data. Audio data was measured by speaking turns, words used, and keywords used, which were defined as our communication dynamics. Our exploratory analysis demonstrated a correlation between the group performance score using communication dynamics metrics, along with movement dynamics metrics. This assessment provided insights for sensing data capture strategies and computational analysis for future small group research studies.

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In general, mixed integer linear programming models are known to be computationally intractable. Algorithmic procedures that provide optimal or near optimal solutions are usually specific to a particular model or class of models. In recent decades, the focus has shifted to developing, what are termed, tight formulations through the use of valid inequalities. Valid inequalities are typically specific to a class of optimization models and have been shown to be crucial to obtaining optimal solutions. Modeling the supply chain to determine efficient and effective policies across and within the echelons of a supply chain can be a formidable task. Supply chains are not only defined by their physical infrastructure of plants, warehouses, distribution centers, and transportation alternatives, but also by defining characteristics such as the number of products, product volumes, and their demand patterns over time. A model for a supply chain can defy solution for specific instances of both the product characteristics and the spatial characteristics of the infrastructure. The difference in two supply chain instances that render the model for one to be intractable, and the model for the other to yield instantaneously obtained optimal solutions, can be due to a combination of factors, such as cost structures, demand patterns, or spatial configurations. This dissertation examines various model instances of each of a suite of single and multi-period supply chain models to reveal the manner in which specific supply chain characteristics can lead to computational intractability. Valid inequalities, which are developed for each model studied, are shown to not only alleviate the model's computational intractability by orders of magnitude, but also afford identification of optimal solutions to a spectrum of model instances. This study of various problem instances reveals that particular patterns of transportation, inventory, and distribution are determined by the particular cost structures and cost tradeoffs. The studies reveal that computational intractability is, in fact, due to factors that include specific types of spatial configuration, cost structures of multiple modes of transportation, and temporal relationships between inventory and transportation.

Given a multi-story facility, the problem is how to design its evacuation system so that, when an emergency occurs, it will take the minimal clearance time to evacuate the occupant population from the facility. This research formulates the design problem of an evacuation planning system of a multi-story facility on a rectilinear metric and presents an approach to its solution based on a tri-partite series of optimization and stochastic models. In order to find the optimal locations of stairwells on each floor, a heuristic for equi-area partitioning for rectilinear simple polygons is developed. The facility evacuation system is studied as a state dependent stochastic model, and a simulation program based on this model is used to evaluate the efficiency of the evacuation system. This research also addresses the problem of how to optimally determine the width of the stairwells with the objective of obtaining the minimal clearance time of the evacuation. Finally, a case study is conducted based on a real world problem - the evaluation of the evacuation process of the New York International Gift Fair held at the Jacob Javits Convention Center. The problem is modeled with the state dependent stochastic model; and an evacuation is conducted and suggested solutions for improvement are recommended.

The recent theories of scale-free and small-world networks have significantly enhanced our understanding of the behavior as well as the vulnerability of many real-world networks. However, the majority of network vulnerability studies focus solely on the topological characteristics. Although the topological structure of a network provides crucial information regarding network vulnerability, the flow on a network is also an important indicator, as are the flow-induced costs and the behavior of the users. Latora and Marchiori (2001, 2002, 2004) proposed a network efficiency measure that is shown to have advantages over several existing network measures. Nevertheless, their measure only considers geodesic information and, therefore, ignores important factors such as flows, costs, and behaviors. The first objective of this dissertation is to construct a network efficiency/performance measure that extends the Latora-Marchiori measure to incorporate such important network factors as flows, costs, and behaviors in order to assess the importance of network components. It is shown that the new network measure has advantages over several existing network measures. Furthermore, the measure is able to handle both fixed and elastic demands as well as static and dynamic networks, with the latter of particular relevance to the Internet. Moreover, it enables a ranking of the importance of network components. In addition, instead of looking at the situation where a network component is completely disrupted, network robustness, another important aspect of the network vulnerability, investigates cases in which network resources are reduced in stressful environments. The second goal of this dissertation is to study transportation network robustness based on the new network efficiency/performance measure in order to investigate the network functionality when the links are partially degraded. I also evaluate transportation network robustness under different user behaviors and with environmental concerns. Furthermore, based on the recent results regarding the supernetwork equivalence between transportation networks and supply chain networks as well as financial networks (Nagurney (2006a) and Liu and Nagurney (2007)), I apply the new network measure to study multitiered financial networks with intermediation. I also propose a novel supply chain model with disruption risks and uncertain demands and define a weighted supply chain network performance measure.